Roland Martin defends Rep. Jasmine Crockett against white progressive critics of her Texas Senate bid, telling them to "sit their a-- down" on Tuesday's episode of "At Our Table."

Abstract--In this letter, we investigate whether classical function allocation--the principle of assigning tasks to either a human or a machine--holds for physical Human-Robot Collaboration, which is important for providing insights for Industry 5.0 to guide how to best augment rather than replace workers. This study empirically tests the applicability of Fitts' List within physical Human-Robot Collaboration, by conducting a user study (N=26, within-subject design) to evaluate four distinct allocations of position/force control between human and robot in an abstract blending task. We hypothesize that the function in which humans control the position achieves better performance and receives higher user ratings. When allocating position control to the human and force control to the robot, compared to the opposite case, we observed a significant improvement in preventing overblending. This was also perceived better in terms of physical demand and overall system acceptance, while participants experienced greater autonomy, more engagement and less frustration. An interesting insight was that the supervisory role (when the robot controls both position and force) was rated second best in terms of subjective acceptance. Another surprising insight was that if position control was delegated to the robot, the participants perceived much lower autonomy than when the force control was delegated to the robot. These findings empirically support applying Fitts' principles to static function allocation for physical collaboration, while also revealing important nuanced user experience trade-offs, particularly regarding perceived autonomy when delegating position control. Received 7 May 2025; accepted 25 October 2025.

Cotton is a major cash crop in the United States, with the country being a leading global producer and exporter. Nearly all U.S. cotton is grown in the Cotton Belt, spanning 17 states in the southern region. Harvesting remains a critical yet challenging stage, impacted by the use of costly, environmentally harmful defoliants and heavy, expensive cotton pickers. These factors contribute to yield loss, reduced fiber quality, and soil compaction, which collectively threaten long-term sustainability. To address these issues, this study proposes a lightweight, small-scale, vision-guided autonomous robotic cotton picker as an alternative. An autonomous system, built on Clearpath's Husky platform and integrated with the CottonEye perception system, was developed and tested in the Gazebo simulation environment. A virtual cotton field was designed to facilitate autonomous navigation testing. The navigation system used Global Positioning System (GPS) and map-based guidance, assisted by an RGBdepth camera and a YOLOv8nseg instance segmentation model. The model achieved a mean Average Precision (mAP) of 85.2%, a recall of 88.9%, and a precision of 93.0%. The GPS-based approach reached a 100% completion rate (CR) within a $(5e-6)^{\circ}$ threshold, while the map-based method achieved a 96.7% CR within a 0.25 m threshold. The developed Robot Operating System (ROS) packages enable robust simulation of autonomous cotton picking, offering a scalable baseline for future agricultural robotics. CottonSim code and datasets are publicly available on GitHub: https://github.com/imtheva/CottonSim

Large Language Models (LLMs) are widely used in complex natural language processing tasks but raise privacy and security concerns due to the lack of identity recognition. This paper proposes a multi-party credible watermarking framework (CredID) involving a trusted third party (TTP) and multiple LLM vendors to address these issues. In the watermark embedding stage, vendors request a seed from the TTP to generate watermarked text without sending the user's prompt. In the extraction stage, the TTP coordinates each vendor to extract and verify the watermark from the text. This provides a credible watermarking scheme while preserving vendor privacy. Furthermore, current watermarking algorithms struggle with text quality, information capacity, and robustness, making it challenging to meet the diverse identification needs of LLMs. Thus, we propose a novel multi-bit watermarking algorithm and an open-source toolkit to facilitate research. Experiments show our CredID enhances watermark credibility and efficiency without compromising text quality. Additionally, we successfully utilized this framework to achieve highly accurate identification among multiple LLM vendors.

We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance is significantly better. The same held true for meta-reinforcement learning agents that differed in whether or not they receive contextual information when performing the task. We evaluated their accuracy in predicting human performance in the task and analyzed their internal representations. The results indicate that contextual cues allow the formation of separate representations in space and time when using different visuomotor mappings, whereas the absence of them favors sharing one representation. While both strategies can allow learning of multiple visuomotor mappings, we showed contextual cues provide a computational advantage in terms of how many mappings can be learned.

-- Design of printed circuit board (PCB) stack - up requires the consideration of characteristic impedance, insertion loss and crosstalk. As there are many parameters in a PCB stack - up design, the optimization of these parameters needs to be efficient and accurate. A le ss optimal stack - up would lead to expensive PCB material choices in high speed designs. In this paper, a n efficient global optimization method using parallel and intelligent Bayesian optimization is proposed for the stripline design . In high speed system design, optimizing printed circuit board (PCB) stack - up is playing a more and more important role in design stage.

ROUND ROCK, TEXAS – The suspect in the deadly string of bombings that terrorized Austin blew himself up early Wednesday as authorities closed in on him, bringing a grisly end to the three-week manhunt. But police warned that there might be more bombs still out there. Authorities identified the suspect only as a 24-year-old white man and said his motive remained a mystery, along with whether he acted alone in the five bombings in Texas' capital and suburban San Antonio that killed two people and wounded four others. Authorities had zeroed in on the man in the last 24 to 36 hours and located his vehicle at a hotel on Interstate 35 in the suburb of Round Rock, Austin Police Chief Brian Manley said at a news conference. They were waiting for ballistic vehicles to arrive to move in for an arrest when his vehicle began to drive away, Manley said.

Here are the largest device recalls in the past 20 years. This handout photo taken and released by Gwangju Bukbu Police Station on Sept. 13, 2016, shows a blown-up Samsung Galaxy Note7 smartphone in Gwangju, 270 kms south of Seoul. The owner claims he received burn injuries on a finger while attempting to extinguish his burning phone after he was jolted from his bed by the sound of an explosion early Sept. 13. SAN FRANCISCO -- Troubles with Samsung's Galaxy Note 7 phones put the South Korean company at the head a long line of companies that have faced major tech recalls. Some go back to the beginnings of the personal computer era, with many linked to overheating and fire danger from malfunctioning lithium ion batteries.